1. Technical Field
This invention pertains in general to modeling behavior for use in predicting the likelihood of conversion in an online advertising campaign, and in particular to modeling behavior based on the presence or absence of a set of features.
2. Description of Related Art
In general, online advertising campaign managers are seeking to maximize the impact of an advertising campaign. One measure of an advertising campaign's impact is the number of conversions. A conversion occurs when a user takes an action deemed desirable by the advertiser, such as buying an advertised product, visiting a website, signing up for a service, etc. By analyzing features of an advertising opportunity and features from the consumption histories of converters versus non-converters, models can be developed to predict whether a particular user is likely to become a converter and/or whether a particular advertising opportunity is likely to result in a conversion. Typically, to analyze these features, a single model is formed for each advertising campaign to use to assess each bidding opportunity for the campaign (i.e., an opportunity to display an advertisement in an available slot, for example on a web page visited by an entity, that is auctioned to the highest bidder). However, in reality, certain features of the bidding opportunity or the entity's media consumption history, such as whether an entity has visited the advertiser's website before, might be so highly relevant to the determination of whether a particular advertising opportunity is likely to result in a conversion that it dwarfs the other signals in the model. In such situations, an improved modeling scheme is desirable to enhance the predictive capabilities of such models and allow for finer grain differentiation between bidding opportunities.